fix: Update to work with transformers changes to attention masks

This commit is contained in:
Michael Poutre
2023-11-13 19:47:56 -08:00
parent 2823a80078
commit 46ae926911
2 changed files with 38 additions and 18 deletions
+37 -18
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@@ -1,10 +1,11 @@
from typing import Optional, Tuple, Union, List, TypedDict
from importlib_metadata import version as import_version
from packaging import version
import torch
from transformers import CLIPTextConfig
from transformers.modeling_outputs import BaseModelOutputWithPooling
from transformers.models.clip.modeling_clip import (
_expand_mask,
CLIPTextEmbeddings,
CLIPTextTransformer,
CLIPTextModel,
@@ -105,6 +106,40 @@ class PrompLangCLIPTextTransformer(CLIPTextTransformer):
def __init__(self, config: CLIPTextConfig):
super().__init__(config)
self.embeddings = PromptLangCLIPTextEmbeddings(config)
self.transformers_version = version.parse(import_version('transformers'))
def process_attention_mask(self, hidden_states, attention_mask, bsz, seq_len):
# Parse the transformer version
input_shape = torch.Size([bsz, seq_len])
v4_30 = version.parse('4.30.0')
v4_35 = version.parse('4.35')
if self.transformers_version < v4_30:
print("Using transformers < 4.30.0")
causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
hidden_states.device)
elif v4_30 <= self.transformers_version < v4_35:
print("Using transformers >= 4.30.0 and <= 4.34.*")
from transformers.models.clip.modeling_clip import _make_causal_mask
causal_attention_mask = _make_causal_mask(input_shape, hidden_states.dtype, device=hidden_states.device)
else:
print("Using transformers >= 4.35")
from transformers.modeling_attn_mask_utils import _create_4d_causal_attention_mask
causal_attention_mask = _create_4d_causal_attention_mask(
input_shape, hidden_states.dtype, device=hidden_states.device
)
# Expand attention_mask if it exists
if attention_mask is not None:
# Import _expand_mask or _prepare_4d_attention_mask based on version
if self.transformers_version < v4_35:
from transformers.models.clip.modeling_clip import _expand_mask
attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
else:
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
return causal_attention_mask, attention_mask
def forward(
self,
@@ -136,24 +171,8 @@ class PrompLangCLIPTextTransformer(CLIPTextTransformer):
bsz = len(input_ids)
# TODO: Properly gather this
seq_len = 77
input_shape = torch.Size([bsz, seq_len])
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
## VERSION DIFF ##
# transformers < 4.30.0
if hasattr(self, "_build_causal_attention_mask"):
print("Using transformers < 4.30.0")
causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(hidden_states.device)
else:
# transformers >= 4.30.0
print("Using transformers >= 4.30.0")
from transformers.models.clip.modeling_clip import _make_causal_mask
causal_attention_mask = _make_causal_mask(input_shape, hidden_states.dtype, device=hidden_states.device)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
causal_attention_mask, attention_mask = self.process_attention_mask(hidden_states, attention_mask, bsz, seq_len)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
+1
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@@ -1 +1,2 @@
lark
packaging